Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal

被引：0

作者：

Zhao, Wei ^{[1
]}

Hu, Jing ^{[1
]}

Jia, Dongya ^{[1
]}

Wang, Hongmei ^{[1
]}

Li, Zhenqi ^{[1
]}

Yan, Cong ^{[1
]}

You, Tianyuan ^{[1
]}

机构：

[1] Guangzhou Shiyuan Elect Co Ltd, Cent Res Inst, Guangzhou 510530, Peoples R China

来源：

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2019年

关键词：

HEARTBEAT CLASSIFICATION; MORPHOLOGY;

D O I：

10.1109/embc.2019.8856650

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V), fusion of a ventricular and normal beat (F), pace beat or fusion of a paced and a normal or beat that cannot be classified (Q). In the work, a deep neural network based method was proposed to classify these five types of heartbeat. After removing the noise from ECG signals by a low-pass filter, the two-lead heartbeat segments with 2-s length were generated on the filtered signals, and classified by an adaptive ResNet model. The proposed method was evaluated on the MIT-BIH Arrhythmia Database with the patient-specific pattern. The overall accuracy was 98.6% and sensitivity of N, S, V, F were 99.4%, 85.4%, 96.6%, 90.6% respectively. Experimental results show that the proposed method achieved a good performance, and would be useful in the clinic practice.

引用

页码：1500 / 1503

页数：4

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